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The Ethereal
Creation of a Deep Convolutional Auto-Encoder in Caffe
December 04, 2015 ยท Declared Dead ยท ๐ International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
"No code URL or promise found in abstract"
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Authors
Volodymyr Turchenko, Artur Luczak
arXiv ID
1512.01596
Category
cs.NE: Neural & Evolutionary
Cross-listed
cs.CV,
cs.LG
Citations
42
Venue
International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications
Last Checked
3 months ago
Abstract
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic auto-encoder on the example of MNIST dataset.
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